发表机构
ISR, Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院系统与机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单目航天器姿态估计中近对称混淆和跟踪错误累积问题，提出假设锚定跟踪框架，利用帧间运动选择姿态假设并锚定SLAM轨迹，无需目标特定训练，显著降低误差并提升速度。
AI 中文摘要
非合作目标的单目6自由度姿态估计对于在轨服务和碎片清除至关重要。单图像估计器可能会混淆近对称的航天器朝向，而跟踪可能会维持错误的姿态。我们提出了假设锚定跟踪（HAT），这是一种因果框架，利用帧间运动在对齐和融合之前从相互竞争的基于CAD的姿态假设中进行选择。HAT并非独立地在每张图像中选择得分最高的假设，而是保留相互竞争的朝向历史，并选择一个姿态来锚定由单目SLAM估计的相对轨迹。稀疏锚点和姿态融合在初始化后提供逐帧估计，而无需修改过去的输出。该方法仅需要校准的RGB序列、公制CAD模型以及目标图像区域，这些区域可由检测或分割提供。预训练的姿态和SLAM网络无需针对特定目标进行训练或微调。我们使用MegaPose和PicoPose评估了两个版本Mega-HAT和Pico-HAT，在SPARK-2024、SwissCube和SHIRT上进行了测试，并使用YCB-Video评估了空间领域之外的性能。使用每种方法的一个时间配置，四个数据集比较的算术平均值显示，相对于独立的MegaPose，Mega-HAT的平均姿态误差降低了9.4%，持续输入FPS提高了3.76倍；相对于独立的PicoPose，Pico-HAT的平均姿态误差降低了23.9%，FPS提高了2.42倍。在SPARK上的Mega-HAT消融实验和一个离线参考评估了组件贡献以及修订过去估计的影响。
英文摘要
Monocular 6-DoF pose estimation of non-cooperative targets is important for on-orbit servicing and debris removal. A single-image estimator can confuse near-symmetric spacecraft orientations, and tracking can preserve an incorrect pose. We present Hypothesis-Anchored Tracking (HAT), a causal framework that uses inter-frame motion to select among competing CAD-based pose hypotheses before alignment and fusion. Rather than independently choosing the highest-scoring hypothesis in each image, HAT retains competing orientation histories and selects a pose to anchor the relative trajectory estimated by monocular SLAM. Sparse anchors and pose fusion provide per-frame estimates after initialization without revising past outputs. The method requires only a calibrated RGB sequence, a metric CAD model, and target image regions, which can be supplied by detection or segmentation. The pretrained pose and SLAM networks require no target-specific training or fine-tuning. We evaluate two versions, Mega-HAT and Pico-HAT, using MegaPose and PicoPose, on SPARK-2024, SwissCube and SHIRT, with YCB-Video assessing performance outside the space domain. Using one temporal configuration per method, the arithmetic means of the four dataset-wise comparisons show 9.4% lower mean pose error and 3.76 times the sustained input FPS for Mega-HAT relative to independent MegaPose, and 23.9% lower mean pose error and 2.42 times the FPS for Pico-HAT relative to independent PicoPose. Mega-HAT ablations on SPARK and an offline reference examine component contributions and the effect of revising past estimates.
Comments8 pages, 3 figures, 4 tables
